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16 results for “precipitation predictable”
Dataset and plot generation script for article "Probabilistic short-range forecasts of high precipitation events : optimal decision thresholds and predictability limits" by Francois Bouttier and Hugo Marchal, submitted in Dec 2023.
<p>Dataset and plot generation script for article "Probabilistic short-range forecasts of high precipitation events : optimal decision thresholds and predictability limits" by Francois Bouttier and Hugo Marchal, submitted in NHESS journal in Dec 2023.</p> <p>For further technical details read the file READMEdata in the zipfile. The script MAKEFIG remakes all the figures from the data.</p> <p>For scientific details read the associated article preprint on the NHESS egusphere website.</p>
Spring Precipitation Amount and Timing Predict Restoration Success in a Semi-Arid Ecosystem Code and Data
<table> <tbody> <tr> <td>The data here is summary data compiled from all years of the project that lead to the publication Spring Precipitation Amount and Timing Predict Restoration Success in a Semi-Arid Ecosystem with the Journal of Applied Ecology and code to analyze these data. Our study was focused on the Northern Great Basin ecosystem. We conducted surveys at 48 sites over the course of five years (2016-2020). All were located on public lands managed by either the Bureau of Land Management, Idaho Department of Lands, or Oregon State Lands Department. We looked at the influence of management, biotic, abiotic and weather variables predicting seedling establishment success, 45 predictor variables in all. Machine learning techniques were used to select most important predictor variables to be used in future work predicting good seedling establishment windows. </td> </tr> </tbody> </table>
Precipitation, low-level jet, and geopotential height data for analyzing sources of predictability in the US northern Great Plains
<p>Dec 15, 2021</p> <p> </p> <p><strong>Precipitation, low-level jet, and geopotential height data for analyzing sources of predictability in the US northern Great Plains</strong></p> <p> </p> <p>Carlos M. Carrillo and Francisco Muñoz-Arriola</p> <p> </p> <p><strong>Motivation</strong></p> <p>The data presented here was used to investigate the uskills of precipitation in the US northern Great Plains, and it can be cited as described below. The original data for producing this data is from the Climate Forecast System (CFS) retrospective reanalysis and reforecast as well as precipitation data from the Climate Prediction Center (CPC) from the National Oceanic and Atmospheric Administration (NOAA). Also, gridded data is from the North American Regional Reanalysis (NARR) from the National Centers for Environmental Prediction (NCEP).</p> <p> </p> <p><strong>License </strong></p> <p>Creative Commons CC-BY</p> <p><strong>Disclaimer</strong></p> <p>The data provided in the files is provided as is. Despite our best efforts at filtering out potential issues, some information could be erroneous.</p> <p><strong>Description of the dataset</strong></p> <p>Files are provided with the following features:</p> <p><strong>List of cases: </strong></p> <p> files.0.00.dy.txt</p> <p><strong>Low-level jet (or the GP-LLJ index)</strong></p> <p>Originally located at /home/cmc542/2019/sum-pred/eof/cfs/0.35.cases/</p> <p>Master file:<strong> LLJ_pc_corr_1D_pdf_full.m</strong></p> <p>With input data</p> <p> from CFS models,</p> <p> eof1.v850.cfs.1982-2009.dy.tar</p> <p> pc1.v850.cfs.1982-2009.dy.tar</p> <p> from NARR model,</p> <p> pc1.vwnd.narr.1982-2009.tar</p> <p><strong>The geopotential height (or CGT index): </strong></p> <p>Originally located at /home/cmc542/2019/sum-pred/eof/cfs/0.35.cases/</p> <p>Master file:<strong> Z200_mode_corr_1D_pdf_full.m</strong></p> <p>With input data</p> <p> xt-reco-z200.Full.123.z200.cfs.1982-2009.12-60.tar</p> <p> xt-reco-z200.Full.z200.narr.1982-2009.bin.tar</p> <p><strong>Precipitation at the US Great Plains:</strong></p> <p>Originally located at /home/cmc542/2019/sum-pred/clim/yrcases</p> <p>Master file: <strong>prec_corr_cfs_1D_pdf_full.m</strong></p> <p>With input data:</p> <p> prec.cfs.MW.1982-2009.tar</p> <p> prec.cpc.MW.1982-2009.tar</p> <p><strong>Correlation patterns:</strong></p> <p> Precipitation: PREC.NGP.corr.txt</p> <p> LLJ: LLJ.pcs.corr.narr.pdf.txt</p> <p> Z200: Z200.pcs.corr.narr.pdf.txt</p> <p> </p> <p><strong>Disclaimer</strong></p> <p>The data provided in the files is provided as is. Despite our best efforts at filtering out potential issues, some information could be erroneous.</p> <p><strong>Description of the dataset</strong></p> <p>Files are provided with the following features:</p> <p><strong>List of cases: </strong></p> <p> files.0.00.dy.txt</p> <p><strong>Low-level jet (or the GP-LLJ index)</strong></p> <p>Originally located at /home/cmc542/2019/sum-pred/eof/cfs/0.35.cases/</p> <p>Master file:<strong> LLJ_pc_corr_1D_pdf_full.m</strong></p> <p>With input data</p> <p> from CFS models,</p> <p><strong> </strong>eof1.v850.cfs.1982-2009.dy.tar</p> <p> pc1.v850.cfs.1982-2009.dy.tar</p> <p> from NARR model,</p> <p> pc1.vwnd.narr.1982-2009.tar</p> <p><strong>The geopotential height (or CGT index): </strong></p> <p>Originally located at /home/cmc542/2019/sum-pred/eof/cfs/0.35.cases/</p> <p>Master file:<strong> Z200_mode_corr_1D_pdf_full.m</strong></p> <p>With input data</p> <p> xt-reco-z200.Full.123.z200.cfs.1982-2009.12-60.tar</p> <p> xt-reco-z200.Full.z200.narr.1982-2009.bin.tar</p> <p><strong>Precipitation at the US Great Plains:</strong></p> <p>Originally located at /home/cmc542/2019/sum-pred/clim/yrcases</p> <p>Master file: <strong>prec_corr_cfs_1D_pdf_full.m</strong></p> <p>With input data:</p> <p> prec.cfs.MW.1982-2009.tar</p> <p> prec.cpc.MW.1982-2009.tar</p> <p><strong>Correlation patterns:</strong></p> <p> Precipitation: PREC.NGP.corr.txt</p> <p> LLJ: LLJ.pcs.corr.narr.pdf.txt</p> <p> Z200: Z200.pcs.corr.narr.pdf.txt</p> <p><strong>Credit</strong></p> <p>Carlos M. Carrillo and Francisco Muñoz-Arriola, 2021: “Sources of Subseasonal Predictability of Rainfall in the Northern Great Plains”, <em>Journal of Applied Meteorology and Climatology</em>. In review.</p> <p><strong>Grant funding</strong></p> <p>This research was funded by the U.S. Geological Survey (USGS), the U.S. Department of Agriculture (USDA), the Daugherty Water for Food Global Institute (DWFI) at the University of Nebraska-Lincoln (UNL), and the UNL’s Layman Award.</p>
Atmospheric_river_precipitation_predictability_data
<p>Data for the manuscript entitled "Predictability of Extreme Precipitation Associated With Atmospheric Rivers in Western U.S. Watersheds".</p> <p> </p> <p>It includes daily precipitation data from WRF and PRISM. Also includes atmospheric river information derived from ARTMIP Tier 1 archive.</p> <p> </p> <p>The tools used to generate the figures in the paper is at: <a href="https://github.com/lucas-uw/Chen-2018-GRL">https://github.com/lucas-uw/Chen-2018-GRL</a></p> <p> </p> <p>If you use this dataset, please cite the following paper:</p> <p> </p> <p>Chen, X., Leung, L. R., Gao, Y., Liu, Y., Wigmosta, M., & Richmond, M. (2018). Predictability of extreme precipitation in western U.S. watersheds based on atmospheric river occurrence, intensity, and duration. Geophysical Research Letters, 45, 11,693–11,701. <a href="http://doi.org/10.1029/2018GL079831">https://doi.org/10.1029/2018GL079831</a></p> <p> </p> <p>Chen, X., Leung, L. R., Wigmosta, M., & Richmond, M. (2019). Impact of Atmospheric Rivers on Surface Hydrological Processes in Western U.S. Watersheds. Journal of Geophysical Research: Atmospheres, <a href="http://doi.org/10.1029/2019JD03468">https://doi.org/10.1029/2019JD03468</a></p>
Permeability Prediction in Rocks Experiencing Mineral Precipitation and Dissolution: A Numerical Study
<p>Data sets for the Publication 'Permeability Prediction in Rocks Experiencing Mineral Precipitation and Dissolution: A Numerical Study' in Water Resources Research.</p>
Winter Precipitation-Type Models for "Evidential Deep Learning: Enhancing Predictive Uncertainty Estimation for Earth System Science Applications"
<p>This contains trained model weights, scalers, and evaluation metrics for the winter precipitation-type models trained as part of the paper "Evidential Deep Learning: Enhancing Predictive Uncertainty Estimation for Earth System Science Applications". </p>
Wood density and leaf size jointly predict woody plant growth rates across (but not within) species along a steep precipitation gradient
<p>1. Functional traits have been proposed to define key dimensions of plant ecological strategies, but we lack consensus on whether traits can accurately predict plant demography. Despite theoretical expectations, it has been challenging to find consistent relationships between functional traits and growth. 2. In this study, we quantified inter- and intraspecific trait variation and individual growth rates of woody plants across a steep moisture gradient that varies 10-fold in annual precipitation (350–3,700 mm) in southern Chile and used a hierarchical Bayesian model to predict growth as a function of trait values. 3. We show that large-leaved species with lower stem tissue density exhibited the fastest growth rates, and these two traits exhibited the highest proportion of interspecific variation. Predictions of growth improved considerably (R2 of the best model increased from 0.28 to 0.49) when species-level multiple traits and their interactions were considered. The inclusion of intraspecific trait variation (ITV), however, did not improve models of growth rate. 4. We found that trait-growth rate relationships were not always consistent across levels of biological organization; relationships observed at the interspecific level did not necessarily hold at the intraspecific level. We found that the relationships between wood density or leaf size and growth were consistent in direction across the precipitation gradient, and the relationships between leaf economics traits and growth were weak and site-specific. 5. Synthesis. Although using more than one functional trait considerably improved growth predictions, wood density and leaf size successfully predicted growth rates across (not within) species, which is consistent with a whole-plant carbon economy. We assert that these two traits are intimately linked and ultimately describe a continuum of plant architecture and carbon economy that covers multiple trait syndromes.</p>
Wood density and leaf size jointly predict woody plant growth rates across (but not within) species along a steep precipitation gradient
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Precipitation predictability drives evolution of drought strategies in the common poppy, Papaver rhoeas
<ol> <li>Current climate change leads to an increase in the frequency and intensity of droughts and to decreased precipitation predictability. The few studies investigating plant evolutionary responses to contrasting predictability regimes showed that intrinsic precipitation predictability shapes plant phenotypic variation, drives evolution of phenotypic plasticity, and can vary in strength and direction of selection. This suggests that the selection pressure induced by decreased precipitation predictability may lead to plants coping better with severe drought events. </li> <li>To investigate whether past precipitation predictability influences plant responses to different drought conditions, we performed a common-garden experiment applying control, short-term and long-term drought treatments on seedlings of <em>Papaver rhoeas</em> (Papaveraceae) whose progenitors experienced less versus more precipitation predictability treatments for three consecutive generations. In addition, to assess whether competition modulates plant responses, half of the plants were grown together with the widespread herbaceous plant <em>Galium album </em>(Rubiaceae).</li> <li>In the presence of long drought episodes, plants whose progenitors experienced less predictable precipitation survived longer than those whose progenitors experienced more predictable precipitation. Furthermore, plants whose progenitors experienced less predictable precipitation had lower biomass, which is likely to reduce water loss via transpiration, and, across all drought treatments, they showed lower root investment. However, no trait differences were detected under competition, indicating that interspecific competition may limit the expression of evolutionary responses to changes induced by precipitation predictability.</li> <li>Altogether our results indicate that lower precipitation predictability mainly promotes the evolution of drought-escape and drought-avoidance strategies. Overall, our experiment highlights that precipitation predictability is an important evolutionary driver of plant functional responses, potentially shifting evolutionary trajectories of plants under increasing intensity of drought events. </li> </ol>
Precipitation predictability drives evolution of drought strategies in the common poppy, Papaver rhoeas
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Data from: Impact of predicted precipitation scenarios on multitrophic interactions
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Data from: Decreased precipitation predictability negatively affects population growth through differences in adult survival
Global climate change is leading to decreased climatic predictability. Theoretical work indicates that changes in the climate's intrinsic predictability will affect population dynamics and extinction, but experimental evidence is scarce. Here, we experimentally tested whether differences in intrinsic precipitation predictability affect population dynamics of the European common lizard (Zootoca vivipara) by simulating more (MP) and less predictable (LP) precipitation in 12 semi-natural populations over 3 years and measuring different vital rates. A seasonal age-structured matrix model was parametrized to assess treatment effects on vital rates and asymptotic population growth (λ). There was a non-significant trend for survival being higher in MP than LP precipitation, and no differences existed in reproductive rates. Small non-significant survival differences in adults explained changes in λ and survival differences among age-classes were in line with predictions from cohort resonance. As a result, λ was significantly higher in MP than LP. This experimentally shows that small effects have major consequences on λ, that forecasted decreases in intrinsic precipitation predictability are likely to exacerbate the current rate of population decline and extinction, and that stage-structured matrix models are required to unravel the aftermath of climate change.
Data and codes for the precipitation prediction
<p>Data and codes for the precipitation prediction task in Hubei Province</p>
Data from: Rapid and positive responses of plants to lower predictability in precipitation
Current climate change is characterized by an increase in weather variability, which includes altered means, variance and predictability of weather parameters, and which may affect an organism's ecology and evolution. Few studies experimentally manipulated the variability of weather parameters, and very little is known about effects of changes in the intrinsic predictability of weather parameters on living organisms. Here we experimentally tested effects of differences in intrinsic precipitation-predictability on two herbaceous plants (Onobrychis viciifolia and Papaver rhoeas). Lower precipitation predictability led to phenological advance and to an increase in reproductive success, and population growth. Both species exhibited rapid transgenerational responses in phenology and fitness-related traits across four generations that mitigated most effects of precipitation-predictability on fitness proxies of ancestors. Transgenerational responses appeared to be the result of changes in phenotypic plasticity rather than local adaptation. They mainly existed with respect to conditions prevailing during early, but not during late growth, suggesting that responses to differences in predictability during late growth might be more difficult. The results show that lower short-term predictability of precipitation positively affected fitness, that rapid transgenerational responses existed, and that different time-scales of predictability (short-term, seasonal, and transgenerational predictability) may affect organisms differently. This shows that the time-scale of predictability should be considered in evolutionary and ecological theories, and in assessments of the consequences of climate change.
Data from: Decreased precipitation predictability negatively affects population growth through differences in adult survival
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Data from: Rapid and positive responses of plants to lower predictability in precipitation
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